Package: spCP
Type: Package
Title: Spatially Varying Change Points
Version: 1.4.0
Date: 2025-09-29
Authors@R: person("Samuel I.", "Berchuck", email = "sib2@duke.edu", role = c("aut", "cre"))
Description: Implements a spatially varying change point model with 
    unique intercepts, slopes, variance intercepts and slopes, and 
    change points at each location. Inference is within the 
    Bayesian setting using Markov chain Monte Carlo (MCMC). The 
    response variable can be modeled as Gaussian (no nugget), 
    probit or Tobit link and the five spatially varying parameter
    are modeled jointly using a multivariate conditional 
    autoregressive (MCAR) prior. The MCAR is a unique process that
    allows for a dissimilarity metric to dictate the local spatial 
    dependencies. Full details of the package can be found in the accompanying vignette.
    Furthermore, the details of the package can be found in the corresponding paper published in Spatial Statistics
    by Berchuck et al (2019): "A spatially varying change points model for monitoring glaucoma 
    progression using visual field data", <doi:10.1016/j.spasta.2019.02.001>.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
NeedsCompilation: yes
Depends: R (>= 3.0.2)
Imports: graphics, grDevices, msm (>= 1.0.0), mvtnorm (>= 1.0-0), Rcpp
        (>= 0.12.9), stats, utils
Suggests: coda, classInt, knitr, rmarkdown, womblR (>= 1.0.3)
LinkingTo: Rcpp, RcppArmadillo (>= 0.7.500.0.0)
VignetteBuilder: knitr
Packaged: 2025-09-29 19:50:32 UTC; sib2
Author: Samuel I. Berchuck [aut, cre]
Maintainer: Samuel I. Berchuck <sib2@duke.edu>
Repository: CRAN
Date/Publication: 2025-09-29 20:20:02 UTC
Built: R 4.6.0; x86_64-apple-darwin20; 2026-04-28 21:39:40 UTC; unix
Archs: spCP.so.dSYM
